Download or read book Portfolio Optimization Using a Block Structure for the Covariance Matrix written by David J. Disatnik and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimating the Covariance Matrix for Portfolio Optimization written by David Disatnik and published by . This book was released on 2006 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: We discuss the estimation of the covariance matrix of stock returns for portfolio optimization and show that for constructing the global minimum variance portfolio (GMVP), there is no statistically-significant gain from using more sophisticated shrinkage estimators instead of simpler portfolios of estimators. We introduce a new quot;two block estimator,quot; which produces - in an unconstrained optimization - a positive GMVP, that can be found analytically and that is sensitive to even small changes in the covariance matrix. For constructing the GMVP, an example of our new estimator performs at least as well as a combination of imposing the short sale constraints and using the sample matrix.

Download or read book Robust Equity Portfolio Management written by Woo Chang Kim and published by John Wiley & Sons. This book was released on 2015-11-25 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive portfolio optimization guide, with provided MATLAB code Robust Equity Portfolio Management + Website offers the most comprehensive coverage available in this burgeoning field. Beginning with the fundamentals before moving into advanced techniques, this book provides useful coverage for both beginners and advanced readers. MATLAB code is provided to allow readers of all levels to begin implementing robust models immediately, with detailed explanations and applications in the equity market included to help you grasp the real-world use of each technique. The discussion includes the most up-to-date thinking and cutting-edge methods, including a much-needed alternative to the traditional Markowitz mean-variance model. Unparalleled in depth and breadth, this book is an invaluable reference for all risk managers, portfolio managers, and analysts. Portfolio construction models originating from the standard Markowitz mean-variance model have a high input sensitivity that threatens optimization, spawning a flurry of research into new analytic techniques. This book covers the latest developments along with the basics, to give you a truly comprehensive understanding backed by a robust, practical skill set. Get up to speed on the latest developments in portfolio optimization Implement robust models using provided MATLAB code Learn advanced optimization methods with equity portfolio applications Understand the formulations, performances, and properties of robust portfolios The Markowitz mean-variance model remains the standard framework for portfolio optimization, but the interest in—and need for—an alternative is rapidly increasing. Resolving the sensitivity issue and dramatically reducing portfolio risk is a major focus of today's portfolio manager. Robust Equity Portfolio Management + Website provides a viable alternative framework, and the hard skills to implement any optimization method.

Download or read book Financial Risk Modelling and Portfolio Optimization with R written by Bernhard Pfaff and published by John Wiley & Sons. This book was released on 2016-08-16 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Financial Risk Modelling and Portfolio Optimization with R, 2nd Edition Bernhard Pfaff, Invesco Global Asset Allocation, Germany A must have text for risk modelling and portfolio optimization using R. This book introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. This edition has been extensively revised to include new topics on risk surfaces and probabilistic utility optimization as well as an extended introduction to R language. Financial Risk Modelling and Portfolio Optimization with R: Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field. Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies. Explores portfolio risk concepts and optimization with risk constraints. Is accompanied by a supporting website featuring examples and case studies in R. Includes updated list of R packages for enabling the reader to replicate the results in the book. Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimization will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.

Download or read book Portfolio Optimization with Noisy Covariance Matrices written by Jose Menchero and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Mean-variance optimization provides a framework for constructing portfolios that have minimum risk for a given level of expected return. The required inputs are the expected asset returns, the asset covariance matrix, and a set of investment constraints. While portfolio optimization always leads to an increase in ex ante risk-adjusted performance, there is no guarantee that this performance improvement carries over ex post. The culprit is that both the expected return forecasts and the asset covariance matrix contain estimation error. In this paper, we explore the impact of sampling error in the covariance matrix when using mean-variance optimization for portfolio construction. In particular, we show that sampling error leads to several adverse effects, such as: (a) under-forecasting of risk, (b) increased out-of-sample volatility, (c) increased leverage and turnover, and (d) inefficient allocation of the risk budget.Moreover, we introduce a new framework to explain and understand the origin of these adverse effects. We decompose the optimal portfolio into an alpha portfolio which explains expected returns, and a hedge portfolio which has zero expected return but serves to reduce portfolio risk. We show that sampling error in the asset covariance matrix leads to systematic biases in the volatility and correlation forecasts of these portfolios.We also provide a geometric interpretation showing how these biases lead to the adverse effects described above.

Download or read book Metaheuristics for Portfolio Optimization written by G. A. Vijayalakshmi Pai and published by John Wiley & Sons. This book was released on 2017-12-27 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is a monograph in the cross disciplinary area of Computational Intelligence in Finance and elucidates a collection of practical and strategic Portfolio Optimization models in Finance, that employ Metaheuristics for their effective solutions and demonstrates the results using MATLAB implementations, over live portfolios invested across global stock universes. The book has been structured in such a way that, even novices in finance or metaheuristics should be able to comprehend and work on the hybrid models discussed in the book.

Download or read book Systematic Trading written by Robert Carver and published by Harriman House Limited. This book was released on 2015-09-14 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is not just another book with yet another trading system. This is a complete guide to developing your own systems to help you make and execute trading and investing decisions. It is intended for everyone who wishes to systematise their financial decision making, either completely or to some degree. Author Robert Carver draws on financial theory, his experience managing systematic hedge fund strategies and his own in-depth research to explain why systematic trading makes sense and demonstrates how it can be done safely and profitably. Every aspect, from creating trading rules to position sizing, is thoroughly explained. The framework described here can be used with all assets, including equities, bonds, forex and commodities. There is no magic formula that will guarantee success, but cutting out simple mistakes will improve your performance. You'll learn how to avoid common pitfalls such as over-complicating your strategy, being too optimistic about likely returns, taking excessive risks and trading too frequently. Important features include: - The theory behind systematic trading: why and when it works, and when it doesn't. - Simple and effective ways to design effective strategies. - A complete position management framework which can be adapted for your needs. - How fully systematic traders can create or adapt trading rules to forecast prices. - Making discretionary trading decisions within a systematic framework for position management. - Why traditional long only investors should use systems to ensure proper diversification, and avoid costly and unnecessary portfolio churn. - Adapting strategies depending on the cost of trading and how much capital is being used. - Practical examples from UK, US and international markets showing how the framework can be used. Systematic Trading is detailed, comprehensive and full of practical advice. It provides a unique new approach to system development and a must for anyone considering using systems to make some, or all, of their investment decisions.

Download or read book Markowitz Portfolio Optimization with Misspecified Covariance Matrices written by Yuan Wang and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider portfolio optimization problems in which the true covariance matrix is misspecified and its value may be obtained by solving a suitably defined learning problem. We consider two types of learning problems to aid in such a resolution: (i) sparse covariance selection; and (ii) sparse precision matrix selection. A tradi- tional sequential approach for addressing such a problem requires first solving the learning problem and then using the solution of this problem in solving the result- ing computational problem. Unfortunately, exact solutions to the learning problem may only be obtained asymptotically; consequently, practical implementations of the sequential approach may provide approximate solutions, at best. Instead, we consider a simultaneous approach that solves both the learning problem and port- folio optimization problems simultaneously. In particular, we use the alternating direction method of multipliers (ADMM) to solve the learning problem while the projected gradient method is used to solve the computational problem. Asymp- totic convergence statements and rate analysis is conducted for the simultaneous scheme. Preliminary numerics on a class of misspecified portfolio optimization problems suggests that the scheme provides accurate solutions with a comparable performance with the sequential approach.

Download or read book High Dimensional Covariance Estimation written by Mohsen Pourahmadi and published by John Wiley & Sons. This book was released on 2013-06-24 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning. Recently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. High-Dimensional Covariance Estimation focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task. High-Dimensional Covariance Estimation features chapters on: Data, Sparsity, and Regularization Regularizing the Eigenstructure Banding, Tapering, and Thresholding Covariance Matrices Sparse Gaussian Graphical Models Multivariate Regression The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.

Download or read book Sensitivity Analysis for Changes in the Covariance Matrix in Portfolio Optimization written by Julia Huang and published by . This book was released on 2001 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning for Asset Managers written by Marcos M. López de Prado and published by Cambridge University Press. This book was released on 2020-04-22 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to "learn" complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.

Download or read book Numerical Methods in Finance written by Paolo Brandimarte and published by John Wiley & Sons. This book was released on 2003-10-13 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: Balanced coverage of the methodology and theory of numerical methods in finance Numerical Methods in Finance bridges the gap between financial theory and computational practice while helping students and practitioners exploit MATLAB for financial applications. Paolo Brandimarte covers the basics of finance and numerical analysis and provides background material that suits the needs of students from both financial engineering and economics perspectives. Classical numerical analysis methods; optimization, including less familiar topics such as stochastic and integer programming; simulation, including low discrepancy sequences; and partial differential equations are covered in detail. Extensive illustrative examples of the application of all of these methodologies are also provided. The text is primarily focused on MATLAB-based application, but also includes descriptions of other readily available toolboxes that are relevant to finance. Helpful appendices on the basics of MATLAB and probability theory round out this balanced coverage. Accessible for students-yet still a useful reference for practitioners-Numerical Methods in Finance offers an expert introduction to powerful tools in finance.

Download or read book Bayesian Methods in Finance written by Svetlozar T. Rachev and published by John Wiley & Sons. This book was released on 2008-02-13 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Methods in Finance provides a detailed overview of the theory of Bayesian methods and explains their real-world applications to financial modeling. While the principles and concepts explained throughout the book can be used in financial modeling and decision making in general, the authors focus on portfolio management and market risk management—since these are the areas in finance where Bayesian methods have had the greatest penetration to date.

Download or read book Optimization Methods in Finance written by Gerard Cornuejols and published by Cambridge University Press. This book was released on 2006-12-21 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization models play an increasingly important role in financial decisions. This is the first textbook devoted to explaining how recent advances in optimization models, methods and software can be applied to solve problems in computational finance more efficiently and accurately. Chapters discussing the theory and efficient solution methods for all major classes of optimization problems alternate with chapters illustrating their use in modeling problems of mathematical finance. The reader is guided through topics such as volatility estimation, portfolio optimization problems and constructing an index fund, using techniques such as nonlinear optimization models, quadratic programming formulations and integer programming models respectively. The book is based on Master's courses in financial engineering and comes with worked examples, exercises and case studies. It will be welcomed by applied mathematicians, operational researchers and others who work in mathematical and computational finance and who are seeking a text for self-learning or for use with courses.

Download or read book Honey I Shrunk the Sample Covariance Matrix written by Olivier Ledoit and published by . This book was released on 2011 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: The central message of this paper is that nobody should be using the sample covariance matrix for the purpose of portfolio optimization. It contains estimation error of the kind most likely to perturb a mean-variance optimizer. In its place, we suggest using the matrix obtained from the sample covariance matrix through a transformation called shrinkage. This tends to pull the most extreme coefficients towards more central values, thereby systematically reducing estimation error where it matters most. Statistically, the challenge is to know the optimal shrinkage intensity, and we give the formula for that. Without changing any other step in the portfolio optimization process, we show on actual stock market data that shrinkage reduces tracking error relative to a benchmark index, and substantially increases the realized information ratio of the active portfolio manager.

Download or read book Covariance Matrix Estimation Methods for Constrained Portfolio Optimization in a South African Setting written by Jaison Pedzisai Madume and published by . This book was released on 2010 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dramatically Improved Portfolio Optimization Results with Noise Filtered Covariance written by Alexander Izmailov and published by . This book was released on 2014 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demonstration that in-sample Markowitz type mean-variance optimization, carried out with noise filtered covariance matrices, results in asset allocation that leads to 2-3 times increase of the Sharpe ratio compared to the same optimization carried out without noise filtering.Demonstration of 2-3 times increase of the Sharpe ratio due to asset allocation obtained via optimization, carried out with noise filtered covariance matrices, for two possible optimization scenarios - maximization of portfolio return at a fixed volatility and minimization of portfolio volatility at a fixed return.